{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Model vectorization\n",
        "\n",
        "> **tldr:** This notebook explains how to use `VectorizedModel` for efficient, vectorized simulations (e.g., over batches or ensembles). You will learn how to create a vectorized model using `VectorizedModel.from_model_with_vectorization` (or the `to_vectorized` helper method). This is done by providing \"vector specs\"—a dictionary that maps slices of the model state (like `Prognostic` or `DynamicInput`) to coordinates to vectorize over. The notebook demonstrates through batch and ensemble examples how the order of operations is critical: updating state (like dynamic inputs) before vectorizing shares that state across all instances, while updating after vectorization allows each instance to use different data.\n",
        "\n",
        "Efficient training and evaluation require running simulations vectorized over axes (like batches or ensembles). NeuralGCM provides the `VectorizedModel` to simplify this process.\n",
        "\n",
        "Outline:\n",
        "1. How to vectorize `Model` instance\n",
        "2. Examples zoo: batched rollout, ensemble rollout"
      ],
      "metadata": {
        "id": "ym7dYqZ5lhl8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports { form-width: \"30%\" }\n",
        "\n",
        "import functools\n",
        "\n",
        "import coordax as cx\n",
        "from flax import nnx\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "import jax_datetime as jdt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import xarray\n",
        "\n",
        "from neuralgcm.experimental.core import api\n",
        "from neuralgcm.experimental.core import coordinates\n",
        "from neuralgcm.experimental.core import diagnostics\n",
        "from neuralgcm.experimental.core import dynamic_io\n",
        "from neuralgcm.experimental.core import random_processes\n",
        "from neuralgcm.experimental.core import module_utils\n",
        "from neuralgcm.experimental.core import observation_operators\n",
        "from neuralgcm.experimental.core import nnx_compat\n",
        "from neuralgcm.experimental.core import typing\n",
        "from neuralgcm.experimental.core import xarray_utils"
      ],
      "metadata": {
        "id": "2Ure6WZ4RZir"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Implementation and helper methods for demo GameOfLife `Model`\n",
        "\n",
        "def calculate_sub_steps(\n",
        "    timestep: np.timedelta64, duration: typing.TimedeltaLike\n",
        ") -> int:\n",
        "  \"\"\"Calculate the number of time-steps required to simulate a time interval.\"\"\"\n",
        "  duration = pd.Timedelta(duration)\n",
        "  time_step_ratio = duration / timestep\n",
        "  if abs(time_step_ratio - round(time_step_ratio)) > 1e-6:\n",
        "    raise ValueError(\n",
        "        f'non-integral time-step ratio: {duration=} is not a multiple of '\n",
        "        f'the internal model timestep {timestep}'\n",
        "    )\n",
        "  return round(time_step_ratio)\n",
        "\n",
        "\n",
        "\n",
        "def unroll_model(\n",
        "    model,\n",
        "    outer_steps: int,\n",
        "    timedelta: typing.TimedeltaLike | None,\n",
        "    observe_fn,\n",
        ") -> tuple[typing.ModelState, typing.Pytree]:\n",
        "  model_state_axes_spec = nnx.StateAxes({typing.SimulationVariable: nnx.Carry, ...: None})\n",
        "  if timedelta is None:\n",
        "    timedelta = model.timestep\n",
        "  inner_steps = calculate_sub_steps(model.timestep, timedelta)\n",
        "\n",
        "  def _inner_step(model):\n",
        "    model.advance()\n",
        "\n",
        "  inner_step_fn = nnx.scan(\n",
        "      _inner_step,\n",
        "      length=inner_steps,\n",
        "      in_axes=model_state_axes_spec,\n",
        "      out_axes=0,\n",
        "  )\n",
        "\n",
        "  def _step(model):\n",
        "    observation = observe_fn(model)\n",
        "    inner_step_fn(model)\n",
        "    return observation\n",
        "\n",
        "  unroll_fn = nnx.scan(\n",
        "      _step,\n",
        "      length=outer_steps,\n",
        "      in_axes=model_state_axes_spec,\n",
        "      out_axes=0,\n",
        "  )\n",
        "  observations = unroll_fn(model)\n",
        "  time = coordinates.TimeDelta(np.arange(outer_steps) * timedelta)\n",
        "  observations = cx.tag(observations, time)\n",
        "  return observations\n",
        "\n",
        "def game_of_life_step(\n",
        "    state: jax.Array,\n",
        "    danger_roll: jax.Array | float = 0.0,\n",
        "    danger_threshold: jax.Array | float = 1.0,\n",
        ") -> jax.Array:\n",
        "  \"\"\"Advances 2d periodic array representing the state of game of life.\"\"\"\n",
        "  alive_neighbors = sum(\n",
        "      jnp.roll(jnp.roll(state, i, axis=0), j, axis=1)\n",
        "      for i in (-1, 0, 1) for j in (-1, 0, 1) if (i != 0 and j != 0)\n",
        "  )\n",
        "  no_accident = jnp.broadcast_to(danger_roll < danger_threshold, state.shape)\n",
        "  return ((alive_neighbors == 3) | (state & (alive_neighbors == 2))) & no_accident\n",
        "\n",
        "\n",
        "def random_life_state(\n",
        "    rng: jax.Array,\n",
        "    world_shape: tuple[int, int],\n",
        ") -> jax.Array:\n",
        "  \"\"\"Generates a random state for the game of life.\"\"\"\n",
        "  return jax.random.bernoulli(rng, shape=world_shape)\n",
        "\n",
        "\n",
        "@nnx_compat.dataclass\n",
        "class GameOfLife(api.Model):\n",
        "  \"\"\"Demo model simulating stochastic game of life with forcing.\"\"\"\n",
        "\n",
        "  x: cx.Coordinate\n",
        "  y: cx.Coordinate\n",
        "  threshold_to_live: dynamic_io.DynamicInputSlice | None = None\n",
        "  random_roll: random_processes.NormalUncorrelated | None = None\n",
        "  world_key: str = 'game'\n",
        "  state_key: str = 'u'\n",
        "\n",
        "  @property\n",
        "  def xy(self):\n",
        "    return cx.compose_coordinates(self.x, self.y)\n",
        "\n",
        "  def __post_init__(self):\n",
        "    self.prognostics = typing.Prognostic({\n",
        "        self.state_key: cx.wrap(np.zeros(self.xy.shape, dtype=bool), self.xy),\n",
        "        'time': cx.wrap(jdt.Datetime.from_isoformat('2000-01-01')),\n",
        "    })\n",
        "\n",
        "  def game_step(self, u, roll, threshold):\n",
        "    game_step_fn = cx.cmap(game_of_life_step, out_axes=u.named_axes)\n",
        "    return game_step_fn(u, roll, threshold).tag(self.xy)\n",
        "\n",
        "  @module_utils.ensure_unchanged_state_structure\n",
        "  def assimilate(self, observations):\n",
        "    data = observations[self.world_key]\n",
        "    self.prognostics.value = dict(sorted({\n",
        "        self.state_key: data[self.state_key],\n",
        "        'time': data['time'],\n",
        "    }.items()))\n",
        "\n",
        "  @module_utils.ensure_unchanged_state_structure\n",
        "  def advance(self):\n",
        "    time = self.prognostics.value['time']\n",
        "    if self.threshold_to_live is not None:\n",
        "      threshold = self.threshold_to_live(time)['zzz'].untag(self.xy)\n",
        "    else:\n",
        "      threshold = 1.0\n",
        "    if self.random_roll is not None:\n",
        "      roll_values = self.random_roll.state_values(coords=self.xy).untag(self.xy)\n",
        "      self.random_roll.advance()\n",
        "    else:\n",
        "      roll_values = 0.0\n",
        "\n",
        "    u = self.prognostics.value[self.state_key].untag(self.xy)\n",
        "    next_u = self.game_step(u, roll_values, threshold)\n",
        "    time = time + self.timestep\n",
        "    self.prognostics.value = dict(sorted({\n",
        "        self.state_key: next_u,\n",
        "        'time': time,\n",
        "    }.items()))\n",
        "\n",
        "  @module_utils.ensure_unchanged_state_structure\n",
        "  def observe(self, query):\n",
        "    prognostic = self.prognostics.value\n",
        "    diagnostic = self.diagnostic_values()\n",
        "    operators = {\n",
        "        self.world_key: observation_operators.DataObservationOperator(prognostic),\n",
        "        'diagnostic': observation_operators.DataObservationOperator(diagnostic),\n",
        "    }\n",
        "    result = {}\n",
        "    for k, q in query.items():\n",
        "      result[k] = operators[k].observe(prognostic, q)\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def timestep(self):\n",
        "    return np.timedelta64(1, 's')"
      ],
      "metadata": {
        "id": "U3SgXXJVrGXQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "59b9bf80-f53f-4a34-a821-42c780bd2bc7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating vectorized model\n",
        "\n",
        "**Key concepts:**\n",
        "* `VectorizedModel.from_model_with_vectorization` classmethod is used to instantiate a vectorized model\n",
        "* `VectorizedModel` wraps a model instance, vectorizing its state and storing metadata about the vectorization axes.\n",
        "* Vectorization metadata is stored as `dict[nnx.filterlib.Filter, cx.Coordinate]` mapping slice of model state to vectorization coordinate"
      ],
      "metadata": {
        "id": "NstaJmIZHzaQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To create a comprehensive example, this demo uses a simple GameOfLife model but includes ad-hoc components for diagnostics, randomness, and dynamic inputs.\n",
        "\n",
        "First we create a `Model` instance"
      ],
      "metadata": {
        "id": "P5NKDDJas0Ln"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# defining coordinate used by the model;\n",
        "x = cx.LabeledAxis('x', np.linspace(0, 1))\n",
        "y = cx.LabeledAxis('y', np.linspace(0, 1))\n",
        "xy = cx.compose_coordinates(x, y)\n",
        "\n",
        "def build_new_model():\n",
        "  # Defining diagnostic module;\n",
        "  count_alive = cx.cmap(lambda x: jnp.sum(x).astype(float))\n",
        "  extract_n_alive = lambda x, *a, **kw: {'n_alive': count_alive(x.untag(xy))}\n",
        "  diagnostic_coords = {'n_alive': cx.Scalar()}\n",
        "  n_alive_diagnostic = diagnostics.InstantDiagnostic(extract_n_alive, diagnostic_coords)\n",
        "\n",
        "  # Instantiating the model.\n",
        "  threshold_to_live = dynamic_io.DynamicInputSlice({'zzz': xy}, 'life')\n",
        "  random_roll = random_processes.NormalUncorrelated(xy, 0, 1, nnx.Rngs(0))\n",
        "  life = GameOfLife(x, y, threshold_to_live, random_roll)\n",
        "\n",
        "  # Attaching diagnostic module;\n",
        "  life = module_utils.with_callback(life, n_alive_diagnostic, 'game_step')\n",
        "  return life\n",
        "\n",
        "\n",
        "life = build_new_model()"
      ],
      "metadata": {
        "id": "kYstq_Jds8Nx",
        "outputId": "8637f975-c135-4fbb-a93d-2fa51f0b17a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:2025-10-16 11:16:06,087:jax._src.xla_bridge:822: Unable to initialize backend 'tpu': UNKNOWN: TPU initialization failed: No jellyfish device found.\n",
            "INFO:2025-10-16 11:16:06,089:jax._src.xla_bridge:822: Unable to initialize backend 'pathways': Could not initialize backend 'pathways'\n",
            "INFO:2025-10-16 11:16:06,090:jax._src.xla_bridge:822: Unable to initialize backend 'proxy': INVALID_ARGUMENT: IFRT proxy server address must be '<transport-type>://<backend-address>' (e.g., 'grpc://localhost'), but got \n",
            "INFO:2025-10-16 11:16:06,091:jax._src.xla_bridge:822: Unable to initialize backend 'mock_tpu': Must pass --mock_tpu_platform flag to initialize the mock_tpu backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at what prognostics of this model look like:\n",
        "(to do this we will extract a SimulationState and peek at prognostics slice)"
      ],
      "metadata": {
        "id": "vY1wCOahuKXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(life.simulation_state.prognostics)"
      ],
      "metadata": {
        "id": "dteCudKztY1Q",
        "outputId": "059a075c-b34b-4102-8195-b51383ff72d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;2;79;201;177mState\u001b[0m\u001b[38;2;255;213;3m({\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "  \u001b[38;2;156;220;254m'wrapped_instance'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;255;213;3m{\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "    \u001b[38;2;156;220;254m'prognostics'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mPrognostic\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 2,502 (2.5 KB)\u001b[0m\n",
            "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m{\u001b[0m\u001b[38;2;207;144;120m'u'\u001b[0m: <Field (\n",
            "        dims=('x', 'y'),\n",
            "        shape=(50, 50),\n",
            "        axes={\n",
            "          'x': LabeledAxis(name='x', ticks=<np float64(50,)>),\n",
            "          'y': LabeledAxis(name='y', ticks=<np float64(50,)>),\n",
            "        },\n",
            "      ), \u001b[38;2;207;144;120m'time'\u001b[0m: <Field dims=() shape=() axes={} >\u001b[38;2;255;213;3m}\u001b[0m\n",
            "    \u001b[38;2;255;213;3m)\u001b[0m\n",
            "  \u001b[38;2;255;213;3m}\u001b[0m\n",
            "\u001b[38;2;255;213;3m})\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that the standard model includes prognostic `u` with dimensions \"x, y\".\n",
        "\n",
        "Now that we have a model instance, we can create a vectorized version of it. This is done by providing a \"vector spec\"—a dictionary mapping the state slices you want to vectorize (e.g., `Prognostic`) to their new coordinate (e.g., batch).\n",
        "\n",
        "As a first example let's vectorize full simulation state over a `batch` axis"
      ],
      "metadata": {
        "id": "cuDO6ydktlYB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch = cx.SizedAxis('batch', 4)\n",
        "vector_specs = {typing.SimulationVariable: batch}\n",
        "v_life = api.VectorizedModel.from_model_with_vectorization(life, vector_specs)"
      ],
      "metadata": {
        "id": "VieKZWVutj-t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at prognostics of this model we note that the state has been replicated:"
      ],
      "metadata": {
        "id": "u1NrXuS5ulI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(v_life.simulation_state.prognostics)"
      ],
      "metadata": {
        "id": "fWGJjMtyuIX-",
        "outputId": "8906fd52-22b4-4e47-ce98-3c7f177944b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;2;79;201;177mState\u001b[0m\u001b[38;2;255;213;3m({\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "  \u001b[38;2;156;220;254m'wrapped_instance'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;255;213;3m{\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "    \u001b[38;2;156;220;254m'prognostics'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mPrognostic\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 10,008 (10.0 KB)\u001b[0m\n",
            "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m{\u001b[0m\u001b[38;2;207;144;120m'time'\u001b[0m: <Field dims=('batch',) shape=(4,) axes={'batch': SizedAxis} >, \u001b[38;2;207;144;120m'u'\u001b[0m: <Field (\n",
            "        dims=('batch', 'x', 'y'),\n",
            "        shape=(4, 50, 50),\n",
            "        axes={\n",
            "          'batch': SizedAxis(name='batch', size=4),\n",
            "          'x': LabeledAxis(name='x', ticks=<np float64(50,)>),\n",
            "          'y': LabeledAxis(name='y', ticks=<np float64(50,)>),\n",
            "        },\n",
            "      )\u001b[38;2;255;213;3m}\u001b[0m\n",
            "    \u001b[38;2;255;213;3m)\u001b[0m\n",
            "  \u001b[38;2;255;213;3m}\u001b[0m\n",
            "\u001b[38;2;255;213;3m})\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vectorization metadata is stored on a `vector_axes` property."
      ],
      "metadata": {
        "id": "6kHBaKbhuro8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(v_life.vector_axes)"
      ],
      "metadata": {
        "id": "pqTB3kAluqds",
        "outputId": "e0bfebd4-c856-43ad-e999-07da665b522d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{<class 'neuralgcm.experimental.core.typing.SimulationVariable'>: coordax.SizedAxis('batch', size=4), Ellipsis: Scalar()}\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "wqR4ovmlKDpb"
      },
      "cell_type": "markdown",
      "source": [
        "Vectorization metadata is used for several purposes:\n",
        "1. It assists with automatic vectorization telling `nnx.vmap` how to map over different model slices\n",
        "2. It is used to robustly \"untag\" and \"tag\" coordinates of the model state before and after vectorized calls"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we were to vectorize model again, the metadata would be combined. For example, adding an ensemble vectorization on top of batch will result in:"
      ],
      "metadata": {
        "id": "2ex20CXfu0_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble = cx.SizedAxis('ensemble', 2)\n",
        "eb_life = v_life.to_vectorized({typing.SimulationVariable: ensemble})\n",
        "print(eb_life.vector_axes)\n",
        "print(eb_life.simulation_state.prognostics)"
      ],
      "metadata": {
        "id": "CsCAz0KtvAkf",
        "outputId": "4bacc183-24be-48e4-8eb0-2e512d74f1f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{<class 'neuralgcm.experimental.core.typing.SimulationVariable'>: CartesianProduct(coordinates=(coordax.SizedAxis('ensemble', size=2), coordax.SizedAxis('batch', size=4))), Ellipsis: Scalar()}\n",
            "\u001b[38;2;79;201;177mState\u001b[0m\u001b[38;2;255;213;3m({\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "  \u001b[38;2;156;220;254m'wrapped_instance'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;255;213;3m{\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "    \u001b[38;2;156;220;254m'prognostics'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mPrognostic\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 20,016 (20.1 KB)\u001b[0m\n",
            "      \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m{\u001b[0m\u001b[38;2;207;144;120m'time'\u001b[0m: <Field (\n",
            "        dims=('ensemble', 'batch'),\n",
            "        shape=(2, 4),\n",
            "        axes={\n",
            "          'batch': SizedAxis(name='batch', size=4),\n",
            "          'ensemble': SizedAxis(name='ensemble', size=2),\n",
            "        },\n",
            "      ), \u001b[38;2;207;144;120m'u'\u001b[0m: <Field (\n",
            "        dims=('ensemble', 'batch', 'x', 'y'),\n",
            "        shape=(2, 4, 50, 50),\n",
            "        axes={'batch': SizedAxis(<2 fields...>), 'ensemble': SizedAxis(<2 fields...>), 'x': LabeledAxis(<2 fields...>), 'y': LabeledAxis(<2 fields...>)},\n",
            "      )\u001b[38;2;255;213;3m}\u001b[0m\n",
            "    \u001b[38;2;255;213;3m)\u001b[0m\n",
            "  \u001b[38;2;255;213;3m}\u001b[0m\n",
            "\u001b[38;2;255;213;3m})\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that above we used a `to_vectorized` method that is a syntactic sugar for model vectorization."
      ],
      "metadata": {
        "id": "bxzO3lgqv6QA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examples Zoo:\n",
        "\n",
        "**Key concepts:**\n",
        "* Vectorized models expect inputs to be compatible with vectorized state\n",
        "* Model methods `assimilate`, `advance`, `observe` are automatically vectorized\n",
        "* Updating state before vectorization is a key pattern for replicating non-vectorized data (e.g., using the same dynamic inputs for an entire batch)\n",
        "\n",
        "This section provides a series of examples to demonstrate how these vectorization mechanics create different simulation settings."
      ],
      "metadata": {
        "id": "xp-W5J5rv26s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a variety of vectorized inputs consumed by examples that follow\n",
        "\n",
        "ones_like = lambda c: cx.wrap(jnp.ones(c.shape), c)\n",
        "t0 = jdt.Datetime.from_isoformat('2000-01-01')\n",
        "timedelta = coordinates.TimeDelta(np.arange(1) * np.timedelta64(1, 's'))\n",
        "init_fn = functools.partial(random_life_state, world_shape=xy.shape)\n",
        "\n",
        "#\n",
        "# Unbatch model inputs.\n",
        "#\n",
        "inputs = {\n",
        "    'game': {\n",
        "        'u': cx.wrap(init_fn(jax.random.key(42)), xy),\n",
        "        'time': cx.wrap(t0)\n",
        "    }\n",
        "}\n",
        "# Dynamic Inputs indicating live-thresholds will depend on `x` here.\n",
        "x_threshold = cx.wrap(np.linspace(0.1, 2.0, x.sizes['x']), x)\n",
        "dynamic_inputs = {\n",
        "    'life': {\n",
        "        'zzz': ones_like(timedelta) * x_threshold * ones_like(y),\n",
        "        'time': cx.wrap(t0 + timedelta.deltas, timedelta),\n",
        "    },\n",
        "}\n",
        "dynamic_inputs = jax.tree.map(jnp.asarray, dynamic_inputs)\n",
        "rngs = cx.wrap(jax.random.key(0))\n",
        "\n",
        "#\n",
        "# Batched model inputs.\n",
        "#\n",
        "b = cx.SizedAxis('batch', 4)\n",
        "brange = jnp.arange(b.size)\n",
        "batch_timedeltas = jdt.Timedelta.from_normalized(0 * brange, brange)\n",
        "batched_inputs = {\n",
        "    'game': {\n",
        "        'u': cx.wrap(jax.vmap(init_fn)(jax.random.split(jax.random.key(42), b.size)), b, xy),\n",
        "        'time': cx.wrap(t0 + batch_timedeltas, b)\n",
        "    }\n",
        "}\n",
        "\n",
        "threshold_variations = cx.wrap(np.linspace(0.3, 1.7, b.size), b)\n",
        "batched_dynamic_inputs = {\n",
        "    'life': {\n",
        "        'zzz': threshold_variations * ones_like(timedelta) * x_threshold * ones_like(y),\n",
        "        'time': cx.wrap(t0 + np.stack([timedelta.deltas]*b.size), b, timedelta),\n",
        "    },\n",
        "}\n",
        "batched_dynamic_inputs = jax.tree.map(jnp.asarray, batched_dynamic_inputs)\n",
        "rng_key = jax.random.split(jax.random.key(0), b.size)\n",
        "b_rngs = cx.wrap(rng_key.reshape(b.shape), b)\n",
        "\n",
        "#\n",
        "# Ensembled model inputs.\n",
        "#\n",
        "e = cx.SizedAxis('ensemble', 2)\n",
        "rng_key = jax.random.split(jax.random.key(0), e.size)\n",
        "e_rngs = cx.wrap(rng_key.reshape(e.shape), e)\n",
        "\n",
        "#\n",
        "# Inputs with ensemble and batch\n",
        "#\n",
        "rng_key = jax.random.split(jax.random.key(0), e.size * b.size)\n",
        "eb_rngs = cx.wrap(rng_key.reshape(e.shape + b.shape), e, b)\n",
        "\n",
        "# Queries\n",
        "diagnostic_query = {'diagnostic': {'n_alive': cx.Scalar()}}\n",
        "observe_query = {'game': {'u': xy}}"
      ],
      "metadata": {
        "id": "T6M6A2cNwugv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 1: Batched Rollout (shared randomness and dynamic data)"
      ],
      "metadata": {
        "id": "85-ha3TzwiNK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "life = build_new_model()\n",
        "life.initialize_random_processes(rngs)\n",
        "life.update_dynamic_inputs(dynamic_inputs)\n",
        "\n",
        "# Note how we use more granular types here to specify vectorization axes, e.g.\n",
        "# (Prognostic, Diagnostic) rather than using a wider scope parent type\n",
        "# SimulationVariable that also includes Randomness.\n",
        "vectorized_life = life.to_vectorized({typing.Prognostic: b, typing.Diagnostic: b})\n",
        "\n",
        "# Run vectorized model methods;\n",
        "vectorized_life.assimilate(batched_inputs)\n",
        "init_observations = vectorized_life.observe(observe_query)\n",
        "vectorized_life.advance()\n",
        "post_advance_observations = vectorized_life.observe(observe_query)"
      ],
      "metadata": {
        "id": "5oWeevFTgTgO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = xarray.concat(\n",
        "    [\n",
        "        init_observations['game']['u'].to_xarray(),\n",
        "        post_advance_observations['game']['u'].to_xarray(),\n",
        "    ],\n",
        "    'step'\n",
        ")\n",
        "ds.plot(x='x', y='y', row='step', col='batch')"
      ],
      "metadata": {
        "colab": {
          "height": 457
        },
        "id": "W0GIN9_B6aX4",
        "outputId": "cca4cd76-7905-417d-b584-ece19bfa2897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x3038fd857740>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 936x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 826
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "observe_fn = lambda model: model.observe(observe_query | diagnostic_query)\n",
        "unroll_fn = functools.partial(unroll_model, observe_fn=observe_fn)\n",
        "observations = vectorized_life.run_vectorized(unroll_fn, b, 6, life.timestep * 2)\n",
        "xarray_utils.fields_to_xarray(observations['game']).u.plot(x='x', y='y', col='timedelta', row='batch')"
      ],
      "metadata": {
        "colab": {
          "height": 889
        },
        "id": "TsNtMIPrw-Yu",
        "outputId": "6af1ca2b-0aee-43bd-e4d3-ec2a4ff30846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x303885b4e6c0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1368x864 with 25 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 855,
              "width": 1228
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xarray_utils.fields_to_xarray(observations['diagnostic']).n_alive.plot(x='timedelta', hue='batch', marker='o')"
      ],
      "metadata": {
        "colab": {
          "height": 347
        },
        "id": "p_XYKIzsnLMR",
        "outputId": "f90a9472-6a39-4d66-d8c8-10a635f8c9c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x30389ef1b590>,\n",
              " <matplotlib.lines.Line2D at 0x30389ba8f920>,\n",
              " <matplotlib.lines.Line2D at 0x3038fdb52ab0>,\n",
              " <matplotlib.lines.Line2D at 0x30389eefb740>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 389
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 2: Batched Rollout (different dynamic data)"
      ],
      "metadata": {
        "id": "bbm3c_VPoQbq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "life = build_new_model()\n",
        "life.initialize_random_processes(rngs)\n",
        "\n",
        "# Include dynamic input to vectorization specs.\n",
        "vectorized_life = life.to_vectorized(\n",
        "    {typing.Prognostic: b, typing.Diagnostic: b, typing.DynamicInput: b}\n",
        ")\n",
        "\n",
        "# update_dynamic_inputs now expects batched inputs.\n",
        "vectorized_life.update_dynamic_inputs(batched_dynamic_inputs)\n",
        "vectorized_life.assimilate(batched_inputs)\n",
        "init_observations = vectorized_life.observe(observe_query)\n",
        "\n",
        "vectorized_life.advance()\n",
        "vectorized_life.advance()\n",
        "vectorized_life.advance()\n",
        "\n",
        "post_advance_observations = vectorized_life.observe(observe_query)"
      ],
      "metadata": {
        "id": "ftunkOdaoQbr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = xarray.concat(\n",
        "    [\n",
        "        init_observations['game']['u'].to_xarray(),\n",
        "        post_advance_observations['game']['u'].to_xarray(),\n",
        "    ],\n",
        "    'step'\n",
        ")\n",
        "ds.coords['step'] = np.arange(2) * 3\n",
        "ds.plot(x='x', y='y', row='step', col='batch')"
      ],
      "metadata": {
        "colab": {
          "height": 457
        },
        "id": "kwjkRRoIofcc",
        "outputId": "c164aa29-e91c-4114-9459-16dd4f22630e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x30389bab0a10>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 826
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the rollout above we can clearly see how different dynamic inputs affected rollouts across the batch dimension.\n",
        "\n",
        "Each batch in this example experienced the same randomness. We accomplished this by carefully selecting the order of execution:\n",
        "1. Initialized randomness on a fresh model\n",
        "2. Replicated variables for prognostics, diagnostics and dynamic inputs\n",
        "3. Updated dynamic inputs with batched data\n",
        "4. Assimilated batched data\n",
        "5. Ran advance vectorized over `b` axis (resulting in reuse of randomness)"
      ],
      "metadata": {
        "id": "qxCVet72y1h7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 3: Ensemble Rollout"
      ],
      "metadata": {
        "id": "OnG9muLZmqro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "life = build_new_model()\n",
        "life.update_dynamic_inputs(dynamic_inputs)\n",
        "life.assimilate(inputs)\n",
        "vectorized_life = life.to_vectorized(\n",
        "    {typing.Prognostic: e, typing.Diagnostic: e, typing.Randomness: e}\n",
        ")\n",
        "\n",
        "# requires batched rngs to initialized vectorized random state.\n",
        "vectorized_life.initialize_random_processes(e_rngs)\n",
        "init_observations = vectorized_life.observe(observe_query)\n",
        "\n",
        "for _ in range(4):\n",
        "  vectorized_life.advance()\n",
        "post_advance_observations = vectorized_life.observe(observe_query)"
      ],
      "metadata": {
        "id": "QDCTTrjVpxcs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = xarray.concat(\n",
        "    [\n",
        "        init_observations['game']['u'].to_xarray(),\n",
        "        post_advance_observations['game']['u'].to_xarray(),\n",
        "    ],\n",
        "    'step'\n",
        ")\n",
        "ds.coords['step'] = np.arange(2) * 4\n",
        "ds.plot(x='x', y='y', row='step', col='ensemble')"
      ],
      "metadata": {
        "colab": {
          "height": 457
        },
        "id": "_-P8mzmpqjLl",
        "outputId": "eadeb868-8b7e-4265-bcd2-677e3a93d4ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x30389eef9f40>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 5 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 458
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "observe_fn = lambda model: model.observe(observe_query | diagnostic_query)\n",
        "unroll_fn = functools.partial(unroll_model, observe_fn=observe_fn)\n",
        "observations = vectorized_life.run_vectorized(unroll_fn, e, 6, life.timestep * 2)\n",
        "xarray_utils.fields_to_xarray(observations['game']).u.plot(x='x', y='y', col='timedelta', row='ensemble')"
      ],
      "metadata": {
        "colab": {
          "height": 457
        },
        "id": "v1-Nrov1LI2d",
        "outputId": "5e7f475b-114c-44ab-d14f-78a3e31ae992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x3038feb55940>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1368x432 with 13 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 1207
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xarray_utils.fields_to_xarray(observations['diagnostic']).n_alive.plot(x='timedelta', marker='o', hue='ensemble')"
      ],
      "metadata": {
        "colab": {
          "height": 313
        },
        "id": "wN4T4i28sCdk",
        "outputId": "f3f0977b-7285-4e88-9c0e-0fe038f6867d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x3038f4818590>,\n",
              " <matplotlib.lines.Line2D at 0x3038f3e39640>]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 389
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 4: Ensemble of batches\n",
        "\n",
        "Now let's produce an example that mimics model training with proper scoring rules: ensemble of batches;\n",
        "This setup is trickier because it requires vectorizing the model twice. We first vectorize over the batch axis, then vectorize the already-vectorized model over the ensemble axis.\""
      ],
      "metadata": {
        "id": "VltRCu42rjsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "life = build_new_model()\n",
        "vectorized_life = life.to_vectorized({\n",
        "    typing.Prognostic: b,\n",
        "    typing.Diagnostic: b,\n",
        "    typing.Randomness: b,\n",
        "    typing.DynamicInput: b,\n",
        "})\n",
        "vectorized_life.update_dynamic_inputs(batched_dynamic_inputs)\n",
        "vectorized_life.assimilate(batched_inputs)\n",
        "vectorized_life = vectorized_life.to_vectorized({\n",
        "    typing.Prognostic: e,\n",
        "    typing.Diagnostic: e,\n",
        "    typing.Randomness: e,\n",
        "})\n",
        "vectorized_life.initialize_random_processes(eb_rngs)\n",
        "for _ in range(4):\n",
        "  vectorized_life.advance()\n",
        "\n",
        "post_advance_observations = vectorized_life.observe(observe_query)\n",
        "xarray_utils.fields_to_xarray(post_advance_observations['game']).u.plot(\n",
        "    x='x', y='y', row='ensemble', col='batch'\n",
        ")"
      ],
      "metadata": {
        "id": "5en2pPiCn0y-",
        "colab": {
          "height": 457
        },
        "outputId": "1c309fa6-d0e3-4601-f58e-0ccfe98967e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x3038865b6780>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 936x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 840
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "observe_fn = lambda model: model.observe(observe_query | diagnostic_query)\n",
        "\n",
        "def unroll_fn(model, outer_steps, timedelta):\n",
        "  return unroll_model(model, outer_steps=outer_steps, timedelta=timedelta, observe_fn=observe_fn)\n",
        "\n",
        "# note that in this doubly vectorized example we pass a cartesian product of\n",
        "# coordinates to `run_vectorized`. This ensures that `unroll_fn` is wrapped in a\n",
        "# nested `vmap` over corresponding slices of the model state.\n",
        "observations = vectorized_life.run_vectorized(\n",
        "    unroll_fn, cx.compose_coordinates(e, b), 6, life.timestep * 2\n",
        ")\n",
        "xarray_utils.fields_to_xarray(observations['game']).u.isel(timedelta=5).plot(x='x', y='y', col='batch', row='ensemble')"
      ],
      "metadata": {
        "colab": {
          "height": 457
        },
        "id": "J1et4IxTU9-B",
        "outputId": "9d8aa07a-e35c-478e-a3a9-cade2de00631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x3038eff59190>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 936x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 423,
              "width": 840
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(\n",
        "    data=xarray_utils.fields_to_xarray(observations['diagnostic']).n_alive.to_dataframe().reset_index(),\n",
        "    x='timedelta',\n",
        "    y='n_alive',\n",
        "    hue='batch',\n",
        "    style='ensemble'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "height": 295
        },
        "id": "IPnSmrne1kql",
        "outputId": "7264a928-ae90-4830-8a58-0b1256881be4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='timedelta', ylabel='n_alive'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 389
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "67BWuvX7PJng"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}
